Thoracic cavity definition for 3D PET/CT analysis and visualization.
Thoracic cavity definition for 3D PET/CT analysis and visualization.
复制标题
3D PET/CT分析和可视化的胸腔腔定义。
DOI：
10.1016/j.compbiomed.2015.04.018
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发表时间：
2015-07
影响因子：
7.7
通讯作者：
Higgins, William E.
中科院分区：
文献类型：
--
作者：
Cheirsilp, Ronnarit;Bascom, Rebecca;Allen, Thomas W.;Higgins, William E.
关键词：
X射线计算机断层扫描（CT）和正电子发射断层扫描（PET）是肺癌管理的标准成像方式。CT提供了感兴趣的诊断区域（ROI）的解剖细节，而PET提供了高度特异性的功能信息。在肺癌管理过程中，患者接受共配准的全身PET/CT扫描对和专用的高分辨率胸部CT扫描。利用这些数据，可以收集多模态PET/CT ROI信息以促进疾病管理。然而，需要对胸腔进行有效的图像分割，以将注意力集中在胸部中央。我们提出了一种自动的方法，从三维CT扫描胸腔分割。然后，我们演示了如何促进三维感兴趣区域的定位和可视化的方法在患者的多模态成像研究。我们的分割方法借鉴了数字拓扑和形态学操作，活动轮廓分析，和关键器官地标。使用大型患者数据库，该方法显示出与地面实况区域的高度一致性，平均覆盖率= 99.2%，泄漏= 0.52%。此外，它还实现了极快的计算。对于PET/CT病变分析，分割方法将全身扫描的ROI搜索空间减少了97.7%，或比肺部掩膜实现的搜索空间大近3倍。尽管有这种降低，我们还是实现了100%的真阳性ROI检测，同时还将假阳性（FP）检测率降低了>5倍。最后，该方法通过消除来自心脏、骨骼和肝脏的假PET-avid遮蔽，极大地改善了PET/CT可视化。特别是，PET MIP视图和融合的PET/CT渲染图描绘了与肺癌评估真正相关的病变和邻近解剖结构的前所未有的清晰度。
X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical detail on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage = 99.2% and leakage = 0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment.
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